Development of a cost analysis-based defect-prediction system with a type error-weighted deep neural network algorithm

With the growing interest in smart factories, defect-prediction algorithms using data analysis techniques are being developed and applied to solve problems caused by defects at manufacturing sites. Cost benefit is an important factor to consider, and can be obtained by applying such algorithms. Existing defect-prediction algorithms usually aim to reduce the error rate of the prediction model, rather than focusing on the cost benefit for the practical application of defect-prediction models. Therefore, this study develops a defect-prediction algorithm considering costs and systematization for field application. To this end, a type error-weighted deep neural network (TEW-DNN) is proposed that applies a loss function to set a different weight for each type error, and cost analysis is conducted to search the optimal type error weight. A cost analysis-based defect-prediction system is designed considering the TEW-DNN algorithm and a cyber-physical system environment. The efficacy of the designed system is demonstrated through a case study involving the application of the system in a die-casting factory in South Korea.